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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Mohapatra, Swagatika Panda, Madhumita Sarangi, Partha Pratim Mishra, Bhabani Shankar Prasad Parihar, Nikhil Sahoo, Biswajit School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Trident Academy of Technology Computer Science & Engineering Bhubaneswar India

ISBN: (纸本)9798350361780

Training a Multilayer Perceptron (MLP) using metaheuristic optimization algorithms serves as a benchmark problem for demonstrating the effectiveness of emerging metaheuristic optimizers. In this study, we explore the efficacy of a novel metaheuristic optimizer namely, Fox algorithm, to optimize the weights and biases of an MLP. However, backpropagation algorithm is a traditional and well-known gradient-based optimizer is commonly used in training MLP, may become trap in local minima in many cases due to complex multimodal training error curves. This issue can diminish the classification accuracy. To overcome this problem, stochastic optimization algorithms play a vital role in improving error convergence and generalization capability of the MLP. In our work, exhaustive experimentation and analysis has been performed using five standard classification medical datasets to show the effectiveness of the Fox algorithm. The experimental results compare with backpropagation (generalized delta rule) and three recently proposed metaheuristic optimizers to evaluate its capability to converge to optimal solutions and enhance the overall performance of the MLP. Based on error convergence rate, accuracy, and F1-score, the proposed approach reveals promising classification performance compared to competitors. © 2024 IEEE.

关键词： Multilayer neural networks

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Clinical Decision Support System for Anemia Identification and Severity Stratification 1

Clinical Decision Support System for Anemia Identification a...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Vidhya Shree, H. Yukthi, H.S. Vandana, W.M. Chirchi, Khushi E. Saha, Jayita Dayananda Sagar University Computer Science and Engineering Bengaluru India Manipal Institute of Technology Bengaluru Computer Science and Engineering Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798350371802

The research addresses the pervasive challenges faced by anemic individuals globally. This innovative solution goes beyond traditional approaches by incorporating real-time data collection, including critical parameters such as Complete Blood Count (CBC), age, and gender providing a dynamic and holistic view of an individual's health status. The integration of CBC data, coupled with demographic information, enhances the precision of the monitoring process, allowing for a more accurate assessment of anemia risk and severity. The collected real-time data is seamlessly transmitted to a centralized cloud platform, where advanced machine learning models, specifically recurrent neural networks (RNNs), come into play. Trained on a diverse dataset that includes CBC counts, age, and gender, these models excel in capturing temporal patterns and subtle variations associated with anemia. The result is a robust classification system that achieves a remarkable 97% accuracy, facilitating early detection and tailored interventions. By combining real-time data collection with sophisticated machine learning and deep learning models, the proposed system represents a significant advancement in addressing the challenges posed by anemia. This holistic and personalized approach not only ensures timely intervention but also contributes to improved healthcare outcomes for individuals affected by this widespread condition. © 2024 IEEE.

关键词： Recurrent neural networks

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Parametrized Optimization Based on an Investigation of Musical Similarities Using SPARK and Hadoop

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SN computer science 2024年第1期5卷 78页

作者： Chaudhary, Savita Karthik, V. Shankar, R. Taranum, Ayesha Naresh, E. Department of Computer Science and Engineering Sir M Visvesvaraya Institute of Technology Bengaluru Bengaluru India Department of Information Science and Engineering Ramaiah Institute of Tech B’lore India Department of Computer Science and Engineering BMS Institute of Technology and Engineering Bengaluru India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The big data processing framework Spark is used to power a parameterizable recommender system that can make recommendations for music based on a user’s individual tastes and take into account a variety of musical tonal qualities. The system as it is presently built is completely scalable, which means that additional songs can be contributed to the data, the cluster size could be increased, and new types of audio information, in addition to more cutting-edge similarity evaluations, may be included. Another issue discussed in this research paper is the parallel collection of required audio characteristics on a computer cluster. Song recommendations for a dataset including more than 114,000 songs may be created on a Spark cluster with 16 nodes in under 12 s by integrating eight distinct audio feature types and similarity assessments. After the features have been retrieved, they are sent to the Spark-based recommender system to be processed. The calculated distance was displayed, examined, and graphically depicted. By computing the distance depending on the melody, rhythmic, and timbral components of the music, the final software controls song suggestion. © 2023, The Author(s).

关键词： Big data Hadoop Music information retrieval Optimization Spark

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Artificial sensory neurons and their applications

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Journal of Semiconductors 2025年第1期46卷 108-128页

作者： Jiale Shao Hongwei Ying Peihong Cheng Lingxiang Hu Xianhua Wei Zongxiao Li Huanming Lu Zhizhen Ye Fei Zhuge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingbo 315201China School of Electronic and Information Engineering Ningbo University of TechnologyNingbo 315211China State Key Laboratory of Environment-friendly Energy Materials Southwest University of Science and TechnologyMianyang 621010China Institute of Wenzhou Zhejiang UniversityWenzhou 325006China State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200072China

With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly *** limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a *** computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI *** neurons and synapses are the two core components of neuromorphic computing *** perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of *** work reviews recent advances in artificial sensory neurons and their ***,biological sensory neurons are briefly ***,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working ***,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and ***,challenges faced by artificial sensory neurons at both device and system levels are summarized.

关键词： artificial sensory neurons artificial perception systems neuromorphic computing artificial intelligence

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Unveiling Themes in Judicial Proceedings: A Cross-Country Study Using Topic Modeling on Legal Documents from India and the UK

Unveiling Themes in Judicial Proceedings: A Cross-Country St...

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Didwania, Krish Toshniwal, Durga Agarwal, Amit Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Indian Institute of Technology Roorkee India

Legal documents are indispensable in every country for legal practices and serve as the primary source of information regarding previous cases and employed statutes. In today’s world, with an increasing number of judicial cases, it is crucial to systematically categorize past cases into subgroups, which can then be utilized for upcoming cases and practices. Our primary focus in this endeavor was to annotate cases using topic modeling algorithms such as Latent Dirichlet Allocation, Non-Negative Matrix Factorization, and BerTopic for a collection of lengthy legal documents from India and the UK. This step is crucial for distinguishing the generated labels between the two countries, highlighting the differences in the types of cases that arise in each jurisdiction. Furthermore, an analysis of the timeline of cases from India was conducted to discern the evolution of dominant topics over the years. © 2024 Copyright for this paper by its authors.

关键词： Non-negative matrix factorization

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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Heterogeneous Image Knowledge Driven Visual Perception

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 255-257页

作者： Lan Yan Wenbo Zheng Fei-Yue Wang IEEE the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous *** the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

关键词： Visual visual knowledge

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Aquify:AI-Enhanced Predictive Analytics Toolkit For Water Contamination 5

Aquify:AI-Enhanced Predictive Analytics Toolkit For Water Co...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Sanath, G. Deekshitha, S. Shravya, H. Jayanthi, M.G. Kannadaguli, Prashanth Cambridge Institute of Technology Computer Science and Engineering Bengaluru India Cambridge Institute of Technology Information Science and Engineering Bengaluru India Senior Data Science Trainer Dhaarini Academy Bengaluru India

ISBN: (纸本)9798331528539

Effective monitoring of water quality remains a problem, in real-time contamination detection and prediction. Despite advancements in the sector, many existing approaches continue to rely on labor-intensive laboratory testing, which limits their capacity to give rapid and accurate results. By leveraging advanced ML/DL models, it is possible to build a more robust and adaptable platform for real-time analysis, prediction, and decision support. Approaches based on models such as Random Forest, XGBoost, CNN, RNN, LSTM, and GRU have been good with the accuracy of water quality predictions. They help identify pollution sources and diagnose water quality issues, providing essential information to stakeholders in sustainable water resource management. The ML and DL approaches help improve water quality monitoring and boost future research in environmental sustainability and natural resource management. © 2024 IEEE.

关键词： Resource allocation

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IDTCKS: An Intelligent Integrative Approach for Disaster Tweet Classification and Disaster-Related Document Classification Using Knowledge-Driven Hybrid Semantics 2nd

IDTCKS: An Intelligent Integrative Approach for Disaster Twe...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Nama, Vihaan Deepak, Gerard Department of Computer Science Engineering RV College of Engineering Bengaluru India Department of Computer Science Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819995172

There is an immediate requirement for a strategic semantically inclined, semantic web accommodating framework for disaster tweet classification and disaster document and newsfeed documents recommendation to perform disaster risk mitigation and management. This framework proposes an IDTCKS framework for disaster management as a domain of choice, wherein an agent-driven model for integrating auxiliary knowledge from several news and Twitter APIs is encompassed. Subsequently, the TF-IDF discovers informative terms from the documents. The frameworks encompass two distinct classifiers, namely the AdaBoost Classifier to classify the disaster tweets using the upper ontology for the disaster domain as the features and upper ontology is considered by using the LDA to the enriched disaster terms. Subsequently, the upper ontology and the LDA-enriched disaster terms are also used as a feature-driven logistic regression classifier that helps in classifying the dataset. Semantic relevance is achieved using Twitter Semantic Similarity and Horn’s Index with a threshold and step deviance measure and overall precision of 97.39, an accuracy of 98.24, and a very low False Negative Rate of 0.01 has been achieved by the IDTCKS model for disaster classification while an overall precision of 96.57, accuracy of 97.65, and a very low False Negative Rate of 0.02 has been achieved by the IDTCKS model for document classification, which performs much better than other baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Disasters

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Enhancing Automatic Image Captioning: A Systematic Analysis of Models, Datasets, and Evaluation Metrics 4

Enhancing Automatic Image Captioning: A Systematic Analysis ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ganesan, Kothai Amutha, S. Saketh, G. Venkata Reddy, G. Vishnu Vardhan Chowdary, G. Harinath Sai, J. Ajay Kpr institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331529635

The fusion of computer vision and natural language processing (NLP) has given rise to the interdisciplinary field of automatic image captioning, which aims to generate descriptive text for images without human intervention. This area of research has attracted considerable interest due to its potential uses in improving accessibility, managing content, and enhancing search engine optimization. Over the past years, there has been a drastic increase in the accuracy and detail of captions thanks to recent advances in deep learning, especially with convolutional neural networks (CNNs), long short-term memory (LSTMs), and recurrent neural networks (RNNs). This research examines cutting-edge approaches to automatic image captioning, including attention mechanisms, models based on transformers, and techniques utilizing reinforcement learning. The effectiveness of these methods was evaluated using benchmark datasets, and their advantages and limitations were assessed. Additionally, the obstacles faced in producing contextually appropriate and linguistically precise captions were explored, such as dealing with intricate scenes, uncommon objects, and abstract ideas. The paper concludes by highlighting potential areas for future research, including learning across multiple modalities, captioning across languages, and incorporating common-sense knowledge to improve caption generation. © 2024 IEEE.

关键词： Deep reinforcement learning

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